Replication Guide for Grist for the Hill

The main dataset used for the analyses in Grist for the Hill, grist_1.dta, can be found in the umbrella folder "House Bills 2309".  This dataset includes the variables listed in the grist_codebook.pdf document in the same folder.  The dataset was constructed from a number of published and unpublished sources, including service dates and personal characteristics for members of Congress from ICPSR Study No. 7803, election returns from the CQ Guide to Elections, committee service provided by David Canon, Garrison Nelson and Charles Stewart III, constituency characteristics (e.g., veteran population, farm value per capita, population) drawn from the U.S. Census and elsewhere, district population provided by Erik Engstrom, and bill introductions and political experience compiled by the authors.  The file, grist_1_codebook.pdf, lists and describes each variable in the dataset.

The following instructions will allow interested users to reproduce all tables and figures in the article and online appendix, and recompile the main dataset from original sources.  Instructions for recompiling the main dataset appear at the end this document.  Each set of materials and commands are organized under subheadings that correspond to the names of folders we used to organize these files on our computers.  We have also included a zipped file, Grist_combined.zip, that includes all files in the dataverse.  In running the command files, the user will need to correct the file paths, locating each file as appropriate.

All of the analyses in the article and supporting information were performed using STATA 13.1 and R version 2.14.1.  All of the analysis datasets, including original sources, are saved as text files, Excel (Microsoft Excel 2010) files or STATA files.


Tables and Figures in the Article and Online Appendix

Grist Figure 1 and Table 1

The top half of the STATA do file, Table 1.do, opens the main dataset, grist_1.dta, collapses individual-level bill sponsorship totals to aggregate observations for the 1881-1931 period, calculates the share of bills handled by 15 House committees, and displays the data contained in the two left-hand columns of Table 1.  The bottom half of Table 1.do opens the main dataset, excludes members who do not serve a full term, tabulates the mean number of all bills proposed by members and bills referred to each of 15 committees, and displays the data contained in the four right-hand columns of Table 1.

The STATA do file, Figure 1.do, opens the main dataset, grist_1.dta, collapses individual-level bill sponsorship totals to congress observations, and graphs a time series of bills handled by each House committees relevant to the 15 examined in the article for the 1881-1931 period.  To create Figure 1, use the STATA graph editor to adjust the size of each graph as directed and save as a TIF file.


Grist Figure 2

The top half of the STATA do file, Figure 2.do, imports the Excel file, vets.xlsx, which contains data on the veterans populations and spending hand-coded from the Annual Report of the Commissioner on Pensions (various years), interpolates these data for missing years and saves the resulting data as vets.dta.  The bottom half of Figure 2.do imports the Excel file, pensionbills.xlsx, which contains counts of pension bills by congress, merges this data with vets.dta, creates variables indicating the change in spending and pension bills, and graphs the time series of the Civil War veterans and pensioners population displayed in Figure 2a and change in spending and pension bills displayed in Figure 2b.  To create Figure 2, use the STATA graph editor to save both panels as TIF files.


Grist Figure 3 and Figure A1

The 15 panels of Figure A1 can be reproduced through the following procedures:

1.  Calculate the standardized difference between members of the relevant committee and non-committee members for all listed individual-level predictors and for each of the 15 issues examined in the article (Committee Differences).
2.  Use Monte Carlo simulations to obtain bootstrapped confidence intervals for these standardized differences (Committee Simulations).
3.  Plot the standardized differences with bootstrapped confidence intervals separately for each of the 15 issues (Figure A1).  Use STATA graph editor to adjust the size of each panel as directed.
4.  Save the standardized differences and confidence intervals of the constituency demand predictors for each of the 15 issues (Constituency Demand Biases).
5.  Plot the standardized differences of the constituency demand predictors with bootstrapped confidence intervals (Figure 3).  Use STATA graph editor to adjust the size of the figure as directed.

Those interested in simply reproducing the panels in Figure 3 and Figure A1 can skip to the material under "Figure 3" and "Figure A1" below.

Committee Differences

The folder, Committee Differences, contains 15 STATA do files, one for each of the 15 issues examined in the article.  The issues are consistently denoted by four-letter abbreviations as follows:
_agri -- Agriculture
_baff -- Military Affairs / Naval Affairs
_bank -- Banking
_bldg -- Public Building
_clms -- War Claims
_faff -- Foreign Affairs
_fish -- Merchant Marine and Fisheries
_iaff -- Indian Affairs
_iimp -- Commerce / Rivers and Harbors
_imig -- Immigration
_labor -- Education and Labor / Labor
_land -- Public Lands
_pens -- Pensions / Invalid Pensions
_post -- Post Office and Roads
_ways -- Ways and Means

The do files use calculate the standardized biases displayed in Figure 3 and Figure A1.  For example, the STATA do file, com_dif_agri.do, opens up the main dataset, grist_1.dta, creates variables for members serving on the Agriculture Committee and non-committee members, and then for both groups calculates the mean and standard deviation of various individual-level predictors.  The standardized difference between the means of both groups are calculated and saved for each predictor and saved in a STATA data file, com_dif_agri.dta.

Running the 15 STATA do files in this folder will reproduce the 15 STATA data files in this folder.

Committee Simulations

The folder, Committee Simulations, contains 15 STATA do files, one for each of the 15 issues examined in the article.  The do files use simulations to calculate bootstrapped confidence intervals for the standardized biases displayed in Figure 3 and Figure A1.  For example, the top half of the STATA do file, com_sim_agri.do, runs a loop program that opens up the main dataset, grist_1.dta, randomly assigns members to two groups equal in size to the population of members of the Agriculture Committee and non-committee members, and then for both groups calculates the mean and standard deviation of various individual-level predictors.  The simulate command is used to repeat this process 10,000 times.  The bottom half of com_sim_agri.do calculates the standardized bias or difference between the groups and identifies the fifth and ninety-fifth percentiles for each predictor.  The simulated values are saved in a STATA data file, com_sim_agri.dta.

Running the 15 STATA do files in this folder will reproduce the 15 STATA data files in this folder.

Figure A1

The folder, Figure 3 and Figure A1, contains 15 do files (one for each of the 15 issues examined in the article) that reproduce the panels in Figure A1.  For example, the top half of the STATA do file, Figure A1 agri.do, merges the saved simulated values (bootstrapped 95 percent confidence intervals) and saved standardized biases for members of the Agriculture Committee and non-committee members.  The bottom half of Figure A1 agri.do formats these data and plots the standardized bias for each predictor with its bootstrapped confidence interval.  The standardized biases and confidence intervals for the constituency demand figures are saved in a separate STATA data file, com_bias_agri.dta, for use in Figure 3.

Running the 15 STATA do files in this folder will reproduce the 15 panels in Figure A1 of the online appendix.

Figure 3

The STATA do file, Figure 3.do, merges the saved standardized biases and bootstrapped confidence intervals for the constituency demand figures for each of the 15 issues examined in the article and then plots them.  Use STATA graph editor to adjust the size of the figure as directed.


Grist Figure 4 and Tables A1 A2 and A3

Tables A1 A2 and A3

The STATA do file, Tables A1 A2 and A3.do, opens the main dataset, grist_1.dta, eliminates extraneous observations, and estimates the three models of private bill introductions displayed in Table A1, the six models of local bill introductions in Table A2, and the six models of policy bill introductions in Table A3, and uses CLARIFY to simulate predicted first differences for each predictor.  The STATA log files, Table A1_private.txt, Table A2_local.txt, and Table A3_policy.txt, save the results of these models and the simulated first differences.  The Excel file, Figure 4 estimates.xlsx, compiles the simulated first differences for each predictor.

Figure 4

The STATA do file, Figure 4.do, opens the Excel file containing the simulated first differences from the negative binomial regression models in Tables A1, A2 and A3, and plots the first differences (with 95 percent critical intervals).  Use STATA graph editor to adjust the size of each panel as directed.


Grist Figure 5 and Tables A4 A5 and A6

Tables A4 A5 and A6

The STATA do file, Tables A4 A5 and A6.do, opens the main dataset, grist_1.dta, eliminates extraneous observations, and estimates the models of private bill introductions displayed in Table A4, the models of local bill introductions in Table A5, and the models of policy bill introductions in Table A6, and uses CLARIFY to simulate predicted first differences for each predictor.  The STATA log files, Table A1_private_4760.txt, Table A1_private_6171.txt, Table A2_local_4760.txt, Table A2_local_6171.txt, Table A3_policy_4760.txt and Table A3_policy_6171.txt, save the results of these models and the simulated first differences.  The Excel file, Figure 5 estimates.xlsx, compiles the simulated first differences for the constituency demand predictors.

Figure 5

The STATA do file, Figure 5.do, opens the Excel file containing the simulated first differences from the negative binomial regression models in Tables A4, A5 and A6, and plots the first differences (with 95 percent critical intervals).  Use STATA graph editor to adjust the size of each panel as directed.


Grist Figure 6 and Tables A7 A8 and A9

Tables A7 A8 and A9

The STATA do file, Tables A7 A8 and A9.do, opens the main dataset, grist_1.dta, defines lagged bill introduction, committee transfer and other variables, and estimates the four models of private bill introductions displayed in Table A7, the seven models of local bill introductions in Table A2, and the eight models of policy and non-private bill introductions in Table A9, and uses CLARIFY to simulate predicted first differences for each predictor.  The STATA log files, Table A7_private.txt, Table A8_local.txt, and Table A9_policy.txt, save the results of these models and the simulated first differences.  The Excel file, Figure 6 estimates.xlsx, compiles the simulated first differences for the committee transfer predictors.

Figure 6

The top section of the STATA do file, Figure 6.do, opens the Excel file containing the simulated first differences from the negative binomial regression models in Tables A1, A2 and A3 (Figure 4 estimates.xlsx), and formats the first differences of the committee service predictors for merging.  The STATA file, Count estimates.dta, compiles the simulated first differences for the committee service predictors

The middle section of the STATA do file, Figure 6.do, opens the Excel file containing the simulated first differences from the difference-in-differences models in Tables A7, A8 and A9 (Figure 6 estimates.xlsx), and plots the first differences of the committee transfer predictors (with 95 percent critical intervals).  Use STATA graph editor to adjust the size of the figure as directed.

The bottom section of the STATA do file, Figure 6.do, merges the simulated first differences from the event count models (Count estimates.dta), and produces the scatterplot of first differences from the event count and difference-in-differences models.  Use STATA graph editor to adjust the figure as directed.


Grist Figure A2

The two panels of Figure A2 can be reproduced through the following procedures:

1.  Calculate the standardized difference between members of the relevant committee and non-committee members for all listed individual-level predictors, for each of the 15 issues examined in the article and separately for the 47th to 60th congresses (Committee Differences 4760) and 61st to 71st congresses (Committee Differences 6171).
2.  Use Monte Carlo simulations to obtain bootstrapped confidence intervals for these standardized differences (Committee Simulations 4760, Committee Simulations 6171).
3.  Save the standardized differences and confidence intervals of the constituency demand predictors for each of the 15 issues (Constituency Demand Biases 4760, Constituency Demand Biases 6171).
4.  Plot the standardized differences of the constituency demand predictors with bootstrapped confidence intervals separately for the two periods (Figure A2).  Use STATA graph editor to adjust the size of the figure as directed.

Those interested in simply reproducing the panels in Figure A2 can skip to the material under "Figure A2" below.

Committee Differences 4760

The folder, Committee Differences 4760, contains 15 STATA do files, one for each of the 15 issues examined in the article for the 47th to 60th congresses.  The issues are consistently denoted by four-letter abbreviations and four-number abbreviations as follows:
_agri_4760 -- Agriculture, 47th to 60th congresses
_baff_4760 -- Military Affairs / Naval Affairs, 47th to 60th congresses
_bank_4760 -- Banking, 47th to 60th congresses
_bldg_4760 -- Public Building, 47th to 60th congresses
_clms_4760 -- War Claims, 47th to 60th congresses
_faff_4760 -- Foreign Affairs, 47th to 60th congresses
_fish_4760 -- Merchant Marine and Fisheries, 47th to 60th congresses
_iaff_4760 -- Indian Affairs, 47th to 60th congresses
_iimp_4760 -- Commerce / Rivers and Harbors, 47th to 60th congresses
_imig_4760 -- Immigration, 47th to 60th congresses
_labor_4760 -- Education and Labor / Labor, 47th to 60th congresses
_land_4760 -- Public Lands, 47th to 60th congresses
_pens_4760 -- Pensions / Invalid Pensions, 47th to 60th congresses
_post_4760 -- Post Office and Roads, 47th to 60th congresses
_ways_4760 -- Ways and Means, 47th to 60th congresses

The do files calculate the standardized biases displayed in the top panel of Figure A2.  For example, the STATA do file, com_dif_agri_4760.do, opens up the main dataset, grist_1.dta, creates variables for members serving on the Agriculture Committee and non-committee members, and then for both groups calculates the mean and standard deviation of various individual-level predictors.  The standardized difference between the means of both groups are calculated and saved for each predictor and saved in a STATA data file, com_dif_agri_4760.dta.

Running the 15 STATA do files in this folder will reproduce the 15 STATA data files in this folder.

Committee Simulations 4760

The folder, Committee Simulations, contains 15 STATA do files, one for each of the 15 issues examined in the article for the 47th to 60th congresses.  The do files use simulations to calculate bootstrapped confidence intervals for the standardized biases displayed in the top panel of Figure A2.  For example, the STATA do file, com_sim_agri_4760.do, runs a loop program that opens up the main dataset, grist_1.dta, randomly assigns members to two groups equal in size to the population of members of the Agriculture Committee and non-committee members for the 47th to 60th congresses, and then for both groups calculates the mean and standard deviation of various individual-level predictors.  The simulate command is used to repeat this process 10,000 times.  The simulated values are saved in a STATA data file, com_sim_agri_4760.dta.

Running the 15 STATA do files in this folder will reproduce the 15 STATA data files in this folder.

Figure A2

The folder, Figure A2, contains two do files (one for each of the two periods) that reproduce the panels in Figure A2.  For example, the top half of the STATA do file, Figure A2 4760.do, opens the saved simulated values (bootstrapped 95 percent confidence intervals) for each committee, identifies the fifth and ninety-fifth percentiles for each predictor and then saves the values for the constituency demand variables in a separate STATA data file, com_bias_agri_4760.dta, for use in Figure A2.  The bottom half of Figure A2 4760.do merges these values and plots the standardized bias for each constituency demand predictor with its bootstrapped confidence interval.

The STATA do file, Figure A2 6171.do, uses identical procedures to plot the standardized bias for each constituency demand predictor with its bootstrapped confidence interval for the 61st to 71st congresses.

Running the two STATA do files in this folder will reproduce the two panels in Figure A2 of the online appendix.  Use STATA graph editor to adjust the size of the figure as directed.

Committee Differences 6171

The folder, Committee Differences 6171, contains 15 STATA do files, one for each of the 15 issues examined in the article for the 61st to 71st congresses.

The do files calculate the standardized biases displayed in the bottom panel of Figure A2.  For example, the STATA do file, com_dif_agri_6171.do, opens up the main dataset, grist_1.dta, creates variables for members serving on the Agriculture Committee and non-committee members, and then for both groups calculates the mean and standard deviation of various individual-level predictors.  The standardized difference between the means of both groups are calculated and saved for each predictor and saved in a STATA data file, com_dif_agri_6171.dta.

Running the 15 STATA do files in this folder will reproduce the 15 STATA data files in this folder.

Committee Simulations 6171

The folder, Committee Simulations, contains 15 STATA do files, one for each of the 15 issues examined in the article for the 61st to 71st congresses.  The do files use simulations to calculate bootstrapped confidence intervals for the standardized biases displayed in the bottom panel of Figure A2.  For example, the STATA do file, com_sim_agri_6171.do, runs a loop program that opens up the main dataset, grist_1.dta, randomly assigns members to two groups equal in size to the population of members of the Agriculture Committee and non-committee members for the 61st to 71st congresses, and then for both groups calculates the mean and standard deviation of various individual-level predictors.  The simulate command is used to repeat this process 10,000 times.  The simulated values are saved in a STATA data file, com_sim_agri_6171.dta.

Running the 15 STATA do files in this folder will reproduce the 15 STATA data files in this folder.


Grist Figure A3

The 15 panels of Figure A3 can be reproduced through the following procedures, which are identical to steps 1-3 used to produce Figure A1:

1.  Calculate the standardized difference between members who transfer off the relevant committee and continuing committee members for all listed individual-level predictors and for each of the 15 issues examined in the article (Transfer Differences).
2.  Use Monte Carlo simulations to obtain bootstrapped confidence intervals for these standardized differences (Transfer Simulations).
3.  Plot the standardized differences with bootstrapped confidence intervals separately for each of the 15 issues (Figure A3).  Use STATA graph editor to adjust the size of each panel as directed.

Those interested in simply reproducing the panels in Figure A3 can skip to the material under "Figure A3" below.

Transfer Differences

The folder, Transfer Differences, contains 15 STATA do files, one for each of the 15 issues examined in the article.

The do files calculate the standardized biases displayed in Figure A3.  For example, the STATA do file, tr_dif_agri.do, opens up the dataset used to estimate the Committee Transfers models in Tables A7, A8 and A9, grist_2_transfers.dta, creates variables for members transferring off the Agriculture Committee and continuing committee members, and then for both groups calculates the mean and standard deviation of various individual-level predictors.  The standardized difference between the means of both groups are calculated and saved for each predictor and saved in a STATA data file, tr_dif_agri.dta.

Running the 15 STATA do files in this folder will reproduce the 15 STATA data files in this folder.

Transfer Simulations

The folder, Transfer Simulations, contains 15 STATA do files, one for each of the 15 issues examined in the article.  The do files use simulations to calculate bootstrapped confidence intervals for the standardized biases displayed in Figure A3.  For example, the top half of the STATA do file, tr_sim_agri.do, runs a loop program that opens up the dataset used to estimate the Committee Transfers models, grist_2_transfers.dta, randomly assigns members to two groups equal in size to the population of members who transfer off the Agriculture Committee and continuing committee members, and then for both groups calculates the mean and standard deviation of various individual-level predictors.  The simulate command is used to repeat this process 10,000 times.  The bottom half of tr_sim_agri.do calculates the standardized bias or difference between the groups and identifies the fifth and ninety-fifth percentiles for each predictor.  The simulated values are saved in a STATA data file, tr_sim_agri.dta.

Running the 15 STATA do files in this folder will reproduce the 15 STATA data files in this folder.

Figure A3

The folder, Figure A3, contains 15 do files (one for each of the 15 issues examined in the article) that reproduce the panels in Figure A3.  For example, the top half of the STATA do file, Figure A3 agri.do, merges the saved simulated values (bootstrapped 95 percent confidence intervals) and saved standardized biases for members who transfer off the Agriculture Committee and continuing committee members.  The bottom half of Figure A3 agri.do formats these data and plots the standardized bias for each predictor with its bootstrapped confidence interval.

Running the 15 STATA do files in this folder will reproduce the 15 panels in Figure A3 of the online appendix.


Grist Figure A4 and Tables A10 A11 and A12

Tables A10 A11 and A12

The STATA do file, Tables A10 A11 and A12.do, opens the main dataset, grist_1.dta, eliminates extraneous observations, and estimates the models of private bill introductions displayed in Table A10, the models of local bill introductions in Table A11, and the models of policy bill introductions in Table A12, and uses CLARIFY to simulate predicted first differences for each predictor.  The STATA log files, Table A10_private.txt, Table A11_local.txt, and Table A12_policy.txt, save the results of these models and the simulated first differences.  The Excel file, Figure A4 estimates.xlsx, compiles the simulated first differences for the constituency demand predictors.

Figure A4

The STATA do file, Figure A4.do, opens the Excel file containing the simulated first differences from the negative binomial regression models in Tables A10, A11 and A12, and plots the first differences (with 95 percent critical intervals).  Use STATA graph editor to adjust the size of each panel as directed.


Grist Tables A13 A14 and A15 and Figures A5 A6 and A7

Tables A13 A14 and A15

The STATA do file, Tables A13 A14 and A15.do, opens the saved dataset, grist_2_transfers.dta, identifies members who experienced a loss of majority status, interacts lost majority status with the committee transfer variables, and estimates the three models of private bill introductions displayed in Table A5, the six models of local bill introductions in Table A5, the six models of policy bill introductions in Table A6, and the four models of all private, all local, all policy and all non-private bill introductions in Table A7, and uses CLARIFY to simulate predicted first differences for each predictor.  The STATA log files, Table A13_private.txt, Table A14_local.txt, and Table A15_policy.txt, save the results of these models and the simulated first differences.  The Excel file, Figures A5 A6 and A7 estimates.xlsx, compiles the simulated first differences for the committee transfer predictors.

Figures A5 A6 and A7

The STATA do file, Figures A5 A6 and A7.do, opens the Excel file containing the simulated first differences from the negative binomial regression models in Tables A13, A14 and A15 (Figures A5 A6 and A7 estimates.xlsx), and plots, issue by issue, the predicted bill counts for continuing members, lost majority and other transfers.  Use STATA graph editor to adjust each figure as directed.


Grist Tables A16 A17 and A18 and Figure A8

Tables A16 A17 and A18

The STATA do file, Tables A16 A17 and A18.do, opens the saved dataset, grist_2_transfers.dta, defines committee transfer and other variables, and estimates the four models of private bill introductions displayed in Table A16, the seven models of local bill introductions in Table A17, and the eight models of policy and non-private bill introductions in Table A18, and uses CLARIFY to simulate predicted first differences for each predictor.  The STATA log files, Table A16 private.txt, Table A17 local.txt, and Table A18 policy.txt, save the results of these models and the simulated first differences.  The Excel file, Figure A8 estimates.xlsx, compiles the simulated first differences for the committee transfer predictors.

Figure A8

The STATA do file, Figure A8.do, opens the Excel file containing the simulated first differences from the difference-in-differences models in Tables A16, A17 and A18 (Figure A8 estimates.xlsx), and plots the first differences of the committee transfer predictors (with 95 percent critical intervals).  Use STATA graph editor to adjust the size of the figure as directed.


Grist Tables A19 A20 and A21 and Figure A9

Tables A19 A20 and A21

The STATA do file, Tables A19 A20 and A21.do, opens the main dataset, grist_1.dta, identifies members whose private bill introductions rank in the top 2.5 percent, creates an exposure term for non-private bill introductions, and estimates the three models of private bill introductions displayed in Table A19, the six models of local bill introductions in Table A20, the six models of policy bill introductions in Table A21, and uses CLARIFY to simulate predicted first differences for each predictor.  The STATA log files, Table A19 private.txt, Table A20 local.txt, and Table A21 policy.txt, save the results of these models and the simulated first differences.  The Excel file, Figure A9 estimates.xlsx, compiles the simulated first differences for the constituency demand predictors from these models and those summarized in Tables A1-A3.

Figure A9

The STATA do file, Figure A9.do, opens the Excel file containing the simulated first differences from the negative binomial regression models in Tables A1-A3 and Tables A19-21 (Figure A9 estimates.xlsx), and plots the first differences of the constituency demand predictors (with 95 percent critical intervals).  Use STATA graph editor to adjust the size of the figure as directed.


Constructing grist_1.dta

The file, grist_1.do, merges variables from the sources described below, eliminates extraneous observations and variables, fills in missing committee assignment and district population values, creates other variables used in the analyses, and writes these data to a saved file, grist_1.dta.  This dataset is used to produce most of the tables and figures in the article and online appendix, as described below.  The codebook file grist_1_codebook.pdf describes all of the variables in the compiled dataset.

Those interested in simply reproducing the panels in in the manuscript and online appendix can skip to the material under "Tables and Figures in the Article and Online Appendix" below.


Committees

The file, com_38_79_bills_mrg.do, imports committee service data compiled by David Canon, Garrison Nelson and Charles Stewart III into STATA, eliminates extraneous observations and variables, creates a number of committee assignment variables used in the analyses, and formats this data for merging into the main dataset (com_38_79_bils.dta and com_38_79_assigns.dta).  This dataset is a member-congress-session-assignment file that lists all member committee assignments during the 1881-1931 period.  The data file we use (hst3879.dat) and its codebook (hst3879.cb) are available for download at http://web.mit.edu/cstewart/www/.


District Population

The file, dist_pop_1.do, formats two congressional district population data files compiled by Erik Engstrom for the years 1860 to 1862 for merging into the main dataset.  These datasets (district_pop_1860_1910.dta and county_population_1910_1962.dta) are state-year-district files that record the populations of most congressional districts during the 1881-1931 period.  The saved files (dist_pop_1_mrg.dta and dist_pop_2_mrg.dta) contain a unique identifier (sdc_id) and one variable (population).  Missing district values were filled in, where possible, using the same original sources:

Joint Committee on Printing.  Official Congressional Directory.  Various editions.  Washington:  Government Printing Office.

Parsons, Stanley B., William W. Beach, and Michael J. Dubin and Karen Toombs Parsons.  1986.  United States Congressional Districts, 1843-1883.  Westport, CT:  Greenwood Press, Inc.

Parsons, Stanley B., Michael J. Dubin, and Karen Toombs Parsons.  1990.  United States Congressional Districts, 1883-1913.  Westport, CT:  Greenwood Press, Inc.


Gubernatorial Returns

The file, cqgov_1.do, takes gubernatorial election returns data from 1856 to 1946 hand-coded by the authors from the CQ Guide to Elections (cqgov_1856_1946.dta), interpolates the data between election years, creates state Democratic and Republican Party two-party gubernatorial vote shares, and formats this data for merging into the main dataset.  The saved file, cqgov_1.dta, contains a unique identifier (icpsr_cong) and two variables (gdem_p_12_ave, grep_p_12_ave).  The data we use are contained in:

Congressional Quarterly, Inc.  2010.  Guide to U.S. Elections.  6th Edition.  Washington:  CQ Press.


Ideal Points

The file, dwnom1_merge.do, opens the STATA data file, HL01113D21_PRES.DTA, which contains DW-NOMINATE scores compiled by Royce Carroll, Jeff Lewis, James Lo, Nolan McCarty Keith Poole and Howard Rosenthal for the 1st to 113th congresses, eliminates extraneous observations and variables, calculates several measures of legislators� ideology, and formats this data for merging into the main dataset (dwnom1_merge.dta).  The data file we use (HL01113D21_PRES.DTA) is available for download at https://legacy.voteview.com/dwnomin.htm.


McKibben

The file, mck_1.do, imports ICPSR Study No. 7803 into STATA, eliminates extraneous observations and variables, creates other variables used in the analyses and saves the data for merge (mck.dta).  ICPSR Study No. 7803, is a member-congress file with service dates and personal characteristics for every member of the U.S. Congress during the 1881-1931 period.  The data file we use (07803-001-Data.txt) and its codebook (McKibben_Codebook.pdf) are available at https://www.icpsr.umich.edu.


MacKenzie

The file, margin_1.dta, contains congressional election returns data hand-coded by the authors from the CQ Guide to Elections for the 1881-1931 period.  The saved file contains a unique identifier (icpsr_cong) and one variable (margin).  The data we use are contained in:

Congressional Quarterly, Inc.  1975.  Congressional Quarterly�s Guide to U.S. Elections.  Washington:  CQ Press.


The file, mackenz_1.dta, contains previous political experience data compiled by the authors from the Biographical Directory of the U.S. Congress for the 1881-1931 period.  The saved file contains a unique identifier (icpsr_cong) and two variables (yr_hse, l_fun).  The Biographical Directory can be accessed online at http://bioguide.congress.gov/biosearch/biosearch.asp.


Supplemental Data

The STATA do files, supp_merge_1.do, supp_merge_2.do and supp_merge_3.do, import, create and manipulate the constituency demand variables used in the article and described on pages 22-25 of the Online Appendix from the sources described below, and writes these data to a saved file, supp_merge_4.dta.  The codebook file supp_merge_4_codebook.pdf describes all of the variables in the compiled dataset.

Those interested in simply reproducing the panels in in the manuscript and online appendix can skip to the material under "Tables and Figures in the Article and Online Appendix" below.

District Veterans Population

The file, supp_merge_1.do, imports hand coded data on district veteran populations for various years into STATA, interpolates the data between years and creates a ranking of each state�s district veteran population, and formats this data for merging into the supplemental data dataset.  The saved file, Vets_District.dta, contains a state-year identifier (state_year) and two variables (i_vets_dist, i_vets_rank2).  The data we use (vets_dist.xlsx) are contained in:

Bureau of Pensions, Department of Interior.  Annual Report of Commissioner of Pensions.  Various years.  Washington:  Government Printing Office.

Civil War Battlefield Acres

The file, supp_merge_1.do, imports hand coded data on Civil War Battlefield acreage into STATA, collapses the data into state totals and creates a measure of each state�s battlefield affected area, and formats this data for merging into the supplemental data dataset.  The saved file, Civil_War_Battlefields.dta, contains a state identifier and three variables (battle_state, acre_study, acre_core).  The data we use (Civil_War_Battlefield_Acres_2.xlsx) are contained in:

Civil War Sites Advisory Commission. 1993. The Civil War Sites Advisory Commission Report on the Nation�s Civil War Battlefields. Washington: Staff of the Civil War Sites Advisory Commission, National Park Service.

Miles of Inland Waterways

The file, supp_merge_1.do, imports hand coded data on inland waterways into STATA, collapses the data into state totals and creates a measure of each state�s miles of navigable rivers, and formats this data for merging into the supplemental data dataset.  The saved file, Miles_Inland_Waterways.dta, contains a state identifier and one variables (nav_length).  The data we use (Miles_Inland_Waterways.xlsx) are contained in:

Inland Waterways Commission. 1908. Preliminary Report of the Inland Waterways Commission. Washington: Government Printing Office.

Indian Acres

The file, supp_merge_1.do, imports hand coded data on land under the jurisdiction of the Office of Indian Affairs into STATA, interpolates the data between years and creates a variable indicating whether a state has some land under the jurisdiction of the Office of Indian Affairs and the amount of such land, and formats this data for merging into the supplemental data dataset.  The saved file, Indian_Land.dta, contains a state-year identifier and two variables (indian_state, ind_acres).  The data we use (Indian_Bureau_Land2.xlsx) are contained in:

Carter, Susan B., Scott Sigmund Gartner, Michael R. Haines, Alan L. Olmstead, Richard Sutch and Gavin Wright.  2006.  Historical Statistics of the United States.  Millennial Edition.  Cambridge:  Cambridge University Press.

Indian Population

The file, supp_merge_1.do, imports hand coded Census data on population of Native Americans in each state into STATA, and formats this data for merging into the supplemental data dataset.  The saved file, Indians.dta, contains a state-year identifier and one variables (ind_pop).  The data we use (Indians2.xlsx) are contained in:

Carter, Susan B., Scott Sigmund Gartner, Michael R. Haines, Alan L. Olmstead, Richard Sutch and Gavin Wright.  2006.  Historical Statistics of the United States.  Millennial Edition.  Cambridge:  Cambridge University Press.

Public Land Acres

The file, supp_merge_1.do, imports hand coded data on the amount of unused and unappropriated public lands in each state into STATA and creates variables indicating whether a state was created out of the public domain and the amount of unused and unappropriated public land (Public_Land.dta).  Subsequent commands interpolate these data between years, and formats this data for merging into the supplemental dataset.  The saved file, Public_Land2.dta, contains a state-year identifier and two variables (land_state, pub_acres).  The data we use (Public_Land2.xlsx) are contained in:

General Land Office, Department of the Interior.  Report of the Commissioner of the General Land Office.  Various years.  Washington:  Government Printing Office.

World War I Shipping

The file, supp_merge_1.do, imports hand coded data on the number of ships ordered by the U.S. Shipping Board and constructed by shipyards in various states during World War I into STATA, collapses the data into state totals, creates variables identifying the 22 states bordering the Pacific and Atlantic oceans, Gulf of Mexico or one of the Great Lakes, and counting the number of ships constructed during World War I, and formats these data for merging into the supplemental data dataset.  The saved file, WWI_Ships.dta, contains a state identifier and one variables (order).  The data we use (WWI_Merchant_Ships.xlsx) are available at: www.ShipBuildingHistory.com.

Post Offices and Revenue

The file, supp_merge_1.do, imports hand coded data on the number of post offices and amount of postal revenue for various years into STATA, interpolates the data between years and creates variables indicating the amount of postal revenue generated in the state and counting the number of post offices per district, and formats this data for merging into the supplemental data dataset.  The saved file, Post_Offices_Revenues.dta, contains a state-year identifier (state_year) and several variables.  The data we use (Post_Offices_Revenues.xlsx) are contained in:

Postmaster General.  Annual Report of the Postmaster General.  Post Office Department.  Various years.  Washington:  Government Printing Office.

Urban Places

The file, supp_merge_1.do, imports hand coded Census data on the urban population for various years into STATA, and creates variables indicating the total number of urban places and number of new urban places for various years, and formats this data for merging into the supplemental data dataset.  The saved file, Urban_Places2.dta, contains a state-year identifier (state_year) and two variables (places, new_places).  The data we use (Urban_Places2.xlsx) are contained in:

Census Bureau, U.S. Department of Commerce.  Sixteenth Census of the United States:  1940.  Volume 1.  1942.  Washington:  Government Printing Office.

Farm Value

The file, supp_merge_1.do, imports hand-coded Census data on the number of farms and value of farm land and buildings (Farms2.xlsx), creates variables indicating the number of farms and value of farm land and buildings for various years, and formats this data for merging into the supplemental data dataset.  The saved file (Farms.dta), contains a state-year identifier (state_year) and two variables (farms, farm_val).  The data we use (Farms2.xlsx) are contained in:

Carter, Susan B., Scott Sigmund Gartner, Michael R. Haines, Alan L. Olmstead, Richard Sutch and Gavin Wright.  2006.  Historical Statistics of the United States.  Millennial Edition.  Cambridge:  Cambridge University Press.

Farm Population

The file, supp_merge_1.do, imports hand-coded Census data on the farm population (Farm_Pop2.xlsx), creates a variable indicating the population of farmers for various years, and formats this data for merging into the supplemental data dataset.  The saved file (Farm_Pop.dta), contains a state-year identifier (state_year) and one variable (farm_pop).  The data we use (Farm_Pop2.xlsx) are contained in:

Carter, Susan B., Scott Sigmund Gartner, Michael R. Haines, Alan L. Olmstead, Richard Sutch and Gavin Wright.  2006.  Historical Statistics of the United States.  Millennial Edition.  Cambridge:  Cambridge University Press.

Banks

The file, supp_merge_1.do, imports hand-coded data on the number of banks and assets (in millions) held by these banks in each state between 1896 and 1955, and formats this data for merging into the supplemental data dataset.  The saved file (Banks.dta), contains a state-year identifier (state_year) and two variables (banks, assets).  The data we use (Banks2.xlsx) are contained in:

Board of Governors of the Federal Reserve System.  1959.  All-Bank Statistics:  United States 1896-1955.  Washington:  Board of Governors of the Federal Reserve System.

Labor Climate

The file, sup_merge_1.do, imports data from Price Fishback, Rebecca Holmes and Samuel Allen on the labor climate in each state into STATA and creates several variables indicating whether a state ranked above the median in the spatial variables compiled by these authors and the number of pages of labor legislation for a particular year.  The saved file, Labor_Climate.dta, contains s state-year identifier and these various measures.  The data we use (Labor_Climate.xlsx) are summarized in:

Fishback, Price V., Rebecca Holmes, and Samuel Allen.  2008.  "Lifting the Curse of Dimensionality:  Measure of the Labor Legislation Climate in the States During the Progressive Era."  NBER Working Paper No. 14167.  Cambridge:  National Bureau of Economic Research

Population

The file, supp_merge_2.do, imports individual state total, urban and foreign born population data from Carter et al.�s Historical Statistics of the United States, interpolates the data between census years, calculates the urban and foreign born shares of state populations, and formats this data for merging into the main dataset (supp_merge_2.dta).  The data on state populations that we use are contained in:

Carter, Susan B., Scott Sigmund Gartner, Michael R. Haines, Alan L. Olmstead, Richard Sutch and Gavin Wright.  2006.  Historical Statistics of the United States.  Millennial Edition.  Cambridge:  Cambridge University Press.

High Revenue State

The top half of the file, supp_merge_3.do, joins the data on state populations with the other constituency demand measures described above, creates additional variables and fills in missing values.  The bottom half of the file identifies states with the greatest amounts of state and local revenues between 1881 and 1931, eliminates extraneous variables and formats this data for merging into the main dataset (supp_merge_4.dta).  The data we use to identify these states are contained in:

Sylla, Richard E., John B. Legler and John Wallis.  1993.  Sources and Uses of Funds in State and Local Governments, 1790-1915 [Computer file].  ICPSR 09728-v1.  Ann Arbor:  Inter-university Consortium for Political and Social Research.

Sylla, Richard E., John B. Legler and John Wallis.  1995.  State and Local Government: Sources and Uses of Funds, Census Statistics, Twentieth Century [Computer file].  ICPSR 06304.  Ann Arbor:  Inter-university Consortium for Political and Social Research.


Bill Introductions

The file, bills_5.dta, contains individual-level bill introductions data compiled by the authors from various editions of the Congressional Record for the 1881-1931 period.  The saved file contains a unique identifier (icpsr_cong) and 18 other variables corresponding to the total number of bills (total), bills addressing 15 issues examined in the article (pensions, claims, military, internal, indian, interior, maritime, postoffrds, publdgs, agriculture, banking, foreign, immigration, labor, taxes), and bills referred to the Judiciary and Claims committees (judiciary, otherclaims).  For details on the compilation of this data, see the article and supporting information.



